AWS, Cloud Computing

3 Mins Read

Advanced Analytics with Amazon Redshift (Analytics as a Service)

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Analytics as a Service (AaaS) is a modern business model that harnesses the power of cloud technology to provide analytical tools on a subscription basis. This method offers companies a cost-effective and scalable solution for building analytical systems. AaaS enables organizations to make data-driven decisions quickly, allowing them to adapt to changes in the market and make strategic choices with confidence.

Amazon Redshift Equipped with features like data sharing, Amazon Redshift ML, Amazon Redshift Spectrum, and Amazon Redshift Serverless, it simplifies application development and seamlessly integrates robust data analytics capabilities into AaaS offerings.

AaaS Delivery models

The AaaS providers are flexible in deploying using three delivery models: Managed Model: In this approach, the AaaS provider handles data processing within the Amazon Redshift data warehouse.

  • Bring-your-own-Redshift (BYOR) Model: Data processing occurs directly within the customer’s Redshift data warehouse.
  • Hybrid Model: This model combines elements of both approaches, tailored to the specific requirements of the customer.
  • These delivery models afford AaaS providers the flexibility to deliver insights to their customers, irrespective of the location of the data warehouse.

Now, we will go through each model in detail.

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Managed Model

In this model, the AaaS provider initiates customer data ingestion into their account, utilizing their dedicated Amazon Redshift data warehouse for subsequent processing. Following data processing, they employ various methods to distribute the results to their client. Amazon Redshift is a robust platform for companies aiming to construct secure multi-tenant applications, guaranteeing robust data isolation, integrity, and confidentiality. Notable features of Amazon Redshift include row-level security (RLS), column-level security (CLS) for meticulous access control, role-based access control (RBAC), and the ability to allocate permissions at both the database and schema levels.


The workflow involves:

Data Acquisition: AaaS providers gather data from various sources like databases, files, and APIs, then ingest it into their Amazon Redshift warehouse.

Data Enrichment: Data undergoes processing within Amazon Redshift to enhance its quality through custom applications or services like Amazon EMR or AWS Glue.

Insights Delivery: Options include sharing enriched data directly with customer Redshift instances, publishing insights on AWS Data Exchange, exposing insights through a web app with Redshift Data API, or connecting to the provider’s Redshift instance via tools like Amazon QuickSight.

Bring your own Redshift (BYOR) Model

When customers maintain their Amazon Redshift data warehouse and prefer to conduct analytics within their platform without data migration, the BYOR (Bring Your Own Redshift) model is employed.

Under the BYOR model, AaaS providers process data and incorporate insights directly into their customer’s data warehouse, ensuring that the data remains within the customer’s accounts. This approach allows customers to leverage analytics capabilities without transferring data externally.


The solution has the following steps:

  • Customer ingests data from various sources into their Amazon Redshift data warehouse.
  • Data undergoes processing:

AaaS provider utilizes AWS PrivateLink for secure access to the Redshift Data API, enabling direct data processing within the customer’s Redshift data warehouse.

  • Scalable data processing is achieved across multiple customers through the same secure channel.
  • Customized data processing logic is applied per customer, ensuring tailored and efficient services.
  • Customer end-users access data from their account using BI tools and analytics applications.
  • Customers have autonomy over how insights are presented to their end-users.

Hybrid Model

Due to varying requirements in aspects such as data security, compliance, and technical know-how, customers present a wide spectrum of needs. To effectively address this diversity and cater to a larger clientele, AaaS providers often adopt a hybrid strategy. This approach combines elements of the managed and BYOR models, allowing for adaptability and the capacity to serve multiple customers.

The diagram below depicts how the AaaS provider delivers insights via the BYOR model for Customers 1 and 4, while utilizing the managed model for Customer 2 and 3, among others.



In this blog, we explored the rising demand for analytics as a service and how providers use Amazon Redshift to deliver insights to clients. We discussed two main delivery models: managed and BYOR, each offering unique benefits like cost-effectiveness and tailored insights. The flexibility of AWS allows for a hybrid model, catering to diverse customer needs and enabling scalable services.

We also introduced the Powered by Amazon Redshift program, supporting AaaS businesses in creating robust analytics applications for enhanced user experiences and business growth.

Drop a query if you have any questions regarding Amazon Redshift and we will get back to you quickly.

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1. What are the key features of Amazon Redshift?

ANS: – Amazon Redshift offers columnar storage, massively parallel processing (MPP), automatic backups, encryption, data compression, and integration with AWS services like Amazon S3, Amazon DynamoDB, and Amazon EMR.

2. How secure is Amazon Redshift?

ANS: – Amazon Redshift provides several security features, including data encryption at rest and in transit, network isolation using Amazon VPC, fine-grained access control with IAM roles and policies, and integration with AWS Key Management Service (KMS) for managing encryption keys.

3. How does Amazon Redshift pricing work?

ANS: – Amazon Redshift pricing is based on several factors, including the type and number of nodes in the cluster, data transfer costs, and storage usage. Users pay an hourly rate for each node type, and storage costs are based on the amount of data stored in the cluster.

WRITTEN BY Lakshmi P Vardhini



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